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Learning analytics has been widely used in the context of language education. Among the studies that have used this approach, many have developed a dashboard that aims to provide students with recommendations based on data so that they can act on these suggestions and improve their performance. To further our understanding of dashboard research, this study aims to replicate an earlier study using a new data mining strategy, association rule mining, to explore if the new strategy can (1) generate comparable results; and (2) provide new insights into feedback uptake in dashboard systems. The original study was conducted with 423 students at a Hong Kong university and implemented a dashboard for a suite of first-year composition courses. It used a classification tree to identify factors that could predict the uptake of tool-based and general recommendations made by the dashboard. After performing association rule mining with the original data set, this study found that this approach allowed for the identification of additional useful factors associated with the uptake of general and tool-based recommendations with a higher accuracy rate. The results of this study provide new insights for dashboard research and showcase the potential use of association rule mining in the context of language education.

Microlearning, delivered through mobile devices, can increase levels of interactivity and engagement by using high-impact learning strategies in media-rich environments. Podcasts, slide presentations, infographics, videos, flashcards, and collaborative spaces are all suitable delivery tools with which one may create engaging learning experiences. Today’s learners are social, mobile, global, digital, and visual. They embrace the possibilities of technology to facilitate their learning. As a result, microlearning is able to leverage familiar, modern learning technology in short bursts to motivate and engage learners while they are on the go. This chapter provides suggestions for practical strategies, tools, and best practices in multimedia design. In doing so, it aims to help educators and learning professionals create and deliver microlearning mechanisms that are optimized for mobile learning.

This innovations in practice article introduces a chatbot that was developed to support and motivate second language learners during the COVID-19 pandemic. The chatbot was designed to facilitate active, out-of-class language learning to supplement in-class input. It can adapt to learners’ abilities and pace by chatting with them, thus providing differentiated instruction. I describe the rationale for the development and implementation of the chatbot and its pedagogical implications. My aim is to motivate and assist chatbot developers and second language teachers in designing and implementing chatbots that will enhance motivation and independent learning in language students.